{"title":"Asymptotically optimal robust information-based quick detection for general stochastic models with nonparametric postchange uncertainty","authors":"V. Girardin, V. Konev, S. Pergamenchtchikov","doi":"10.1080/07474946.2022.2043052","DOIUrl":null,"url":null,"abstract":"Abstract By making use of Kullback-Leibler information, we develop a new approach for the quickest detection problem for general statistical models with dependent observations and unknown postchange distributions; the postchange distribution depends on either unknown informative parameters or unknown nonparametric infinite-dimensional nuisance functions. For such models, we introduce a robust risk as the supremum of the mean detection delay over the class of postchange distributions. On the basis of the window-limited cumulative sum rules developed by Lai in 1988, we propose new detection procedures, making use of the noise density that minimizes the Kullback-Leibler divergence. Then for the constructed detection procedures, we provide sufficient conditions on the considered statistical models that ensure minimax optimality properties with respect to the robust risk. We apply the developed methods to the quick detection problems for both scalar and multivariate autoregressive processes with unknown postchange parameters and unknown noise distributions.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"41 1","pages":"119 - 141"},"PeriodicalIF":0.6000,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sequential Analysis-Design Methods and Applications","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/07474946.2022.2043052","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract By making use of Kullback-Leibler information, we develop a new approach for the quickest detection problem for general statistical models with dependent observations and unknown postchange distributions; the postchange distribution depends on either unknown informative parameters or unknown nonparametric infinite-dimensional nuisance functions. For such models, we introduce a robust risk as the supremum of the mean detection delay over the class of postchange distributions. On the basis of the window-limited cumulative sum rules developed by Lai in 1988, we propose new detection procedures, making use of the noise density that minimizes the Kullback-Leibler divergence. Then for the constructed detection procedures, we provide sufficient conditions on the considered statistical models that ensure minimax optimality properties with respect to the robust risk. We apply the developed methods to the quick detection problems for both scalar and multivariate autoregressive processes with unknown postchange parameters and unknown noise distributions.
期刊介绍:
The purpose of Sequential Analysis is to contribute to theoretical and applied aspects of sequential methodologies in all areas of statistical science. Published papers highlight the development of new and important sequential approaches.
Interdisciplinary articles that emphasize the methodology of practical value to applied researchers and statistical consultants are highly encouraged. Papers that cover contemporary areas of applications including animal abundance, bioequivalence, communication science, computer simulations, data mining, directional data, disease mapping, environmental sampling, genome, imaging, microarrays, networking, parallel processing, pest management, sonar detection, spatial statistics, tracking, and engineering are deemed especially important. Of particular value are expository review articles that critically synthesize broad-based statistical issues. Papers on case-studies are also considered. All papers are refereed.